NM000205: eeg dataset, 14 subjects#

RSVP collaborative BCI dataset from Zheng et al 2020

Access recordings and metadata through EEGDash.

Citation: Li Zheng, Sen Sun, Hongze Zhao, Weihua Pei, Hongda Chen, Xiaorong Gao, Lijian Zhang, Yijun Wang (2020). RSVP collaborative BCI dataset from Zheng et al 2020.

Modality: eeg Subjects: 14 Recordings: 84 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000205

dataset = NM000205(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000205(cache_dir="./data", subject="01")

Advanced query

dataset = NM000205(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000205,
  title = {RSVP collaborative BCI dataset from Zheng et al 2020},
  author = {Li Zheng and Sen Sun and Hongze Zhao and Weihua Pei and Hongda Chen and Xiaorong Gao and Lijian Zhang and Yijun Wang},
}

About This Dataset#

RSVP collaborative BCI dataset from Zheng et al 2020

RSVP collaborative BCI dataset from Zheng et al 2020.

Dataset Overview

Code: Zheng2020
Paradigm: p300
DOI: 10.3389/fnins.2020.579469

View full README

RSVP collaborative BCI dataset from Zheng et al 2020

RSVP collaborative BCI dataset from Zheng et al 2020.

Dataset Overview

Code: Zheng2020
Paradigm: p300
DOI: 10.3389/fnins.2020.579469
Subjects: 14
Sessions per subject: 2
Events: Target=2, NonTarget=1
Trial interval: [0, 1] s
Runs per session: 3
File format: MATLAB

Acquisition

Sampling rate: 1000.0 Hz
Number of channels: 62
Channel types: eeg=62
Channel names: FP1, FPz, FP2, AF3, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, O1, CB1, Oz, O2, CB2
Montage: standard_1020
Hardware: Neuroscan Synamps2
Reference: vertex (Cz)
Line frequency: 50.0 Hz

Participants

Number of subjects: 14
Health status: healthy
Age: mean=24.9, min=23, max=29
Gender distribution: female=10, male=4
Handedness: all right-handed
Species: human

Experimental Protocol

Paradigm: p300
Number of classes: 2
Class labels: Target, NonTarget
Trial duration: 1.0 s
Study design: RSVP target detection (human vs non-human images); 14 subjects in 7 pairs, synchronized EEG recording
Feedback type: visual
Stimulus type: RSVP images
Stimulus modalities: visual
Primary modality: visual
Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

Target

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Target

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Paradigm-Specific Parameters

Detected paradigm: p300
Stimulus onset asynchrony: 100.0 ms

Data Structure

Trials: {‘target’: 168, ‘nontarget’: 4032}
Trials context: per subject across both sessions

Signal Processing

Classifiers: HDCA
Feature extraction: SIM, CSP, TRCA, PCA
Frequency bands: bandpass=[2.0, 30.0] Hz
Spatial filters: SIM, CSP, PCA, CAR, TRCA

Cross-Validation

Method: holdout
Evaluation type: within_subject, cross_session

BCI Application

Applications: target_image_detection, collaborative_BCI
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: ERP
Type: RSVP

Documentation

DOI: 10.3389/fnins.2020.579469
License: CC-BY-4.0
Investigators: Li Zheng, Sen Sun, Hongze Zhao, Weihua Pei, Hongda Chen, Xiaorong Gao, Lijian Zhang, Yijun Wang
Institution: Chinese Academy of Sciences
Country: CN
Data URL: https://figshare.com/articles/dataset/12824771
Publication year: 2020

References

Zheng, L., Sun, S., Zhao, H., et al. (2020). A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation. Frontiers in Neuroscience, 14, 579469. https://doi.org/10.3389/fnins.2020.579469 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000205`
Title	RSVP collaborative BCI dataset from Zheng et al 2020
Author (year)	`Zheng2020`
Canonical	—
Importable as	`NM000205`, `Zheng2020`
Year	2020
Authors	Li Zheng, Sen Sun, Hongze Zhao, Weihua Pei, Hongda Chen, Xiaorong Gao, Lijian Zhang, Yijun Wang
License	CC-BY-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 14
Recordings: 84
Tasks: 1

Channels & sampling rate

Channels: 62
Sampling rate (Hz): 1000.0
Duration (hours): 8.461972777777778

Electrode Layout#

Electrode layout — EEG · 60 sensors — 60 channels

Dataset Statistics#

Age distribution (n=14, range 24–24 yr)

20

Channel counts: 62 ch (n=84 recordings)

Sampling frequencies: 1000.0 Hz (n=84 recordings)

Total recording duration: 8 h 27 min

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000205 class to access this dataset programmatically.

class eegdash.dataset.NM000205(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

RSVP collaborative BCI dataset from Zheng et al 2020

Study:: nm000205 (NeMAR)
Author (year):: Zheng2020
Canonical:: —

Also importable as: NM000205, Zheng2020.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 14; recordings: 84; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000205 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000205

Examples

>>> from eegdash.dataset import NM000205
>>> dataset = NM000205(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)